System and method for real-time guidance and mapping of a tunnel boring machine and tunnel

ABSTRACT

A system and methods are disclosed for providing the location of a tunnel boring machine (TBM) by establishing of a plurality of known locations or “monuments”; from these monuments located at least on, over or within the TBM&#39;s start point, known in the art as a “pit”. The present invention provides among other things an integrated navigation system that provides real-time parametric guidance information to the TBM, relative to the tunnel origin, past course and current trajectory, while simultaneously employing a non-contact measuring system in concert with said origin and course information for the final provision of an as-built map of tunnel dimensions and centerline.

COPYRIGHT NOTICE

Dan Alan Preston US Citizen US Resident Bainbridge Island, WA JosephDavid Preston US Citizen US Resident Bainbridge Island, WA Marc A.Derenburger US Citizen US Resident Bremerton, WA Carin L. Douglass USCitizen US Resident Silverdale, WA Paul M. Peterson US Citizen USResident Bremerton, WA Kyle A. Yeats US Citizen US Resident PortOrchard, WA

Contained herein is material that is subject to copyright protection.The copyright owner has no objection to the facsimile reproduction byanyone of the patent document or the patent disclosure, as it appears inthe United States Patent and Trademark Office patent file or records,but otherwise reserves all rights to the copyright whatsoever. Thefollowing notice applies to the software, screenshots and data asdescribed below and in the drawings hereto and All Rights Reserved.

FIELD OF THE INVENTION

This invention generally relates to the establishment of a plurality ofknown locations known in the art as “monuments”; from these monumentslocated at least on, over, or within the tunnel boring machine's (TBM)start point, known in the art as a “launch start”. The present inventionprovides among other things an integrated navigation system thatprovides real-time parametric guidance information to the TBM, relativeto the tunnel origin, past course and current trajectory, whilesimultaneously employing a non-contact measuring system in concert withsaid origin and course information for the final provision of anas-built map of tunnel dimensions and centerline.

BACKGROUND OF THE INVENTION

Tunnel boring machines (TBM) are used to excavate circular cross sectiontunnels through a variety of soil and rock strata. As tunnels are boredregardless of geology, it is imperative the TBM and resulting excavatingtunnel stay on the design alignment within the mandated tolerances. Itmay be very costly if 1. The tunnel veers off alignment wanderingoutside of the client's purchased Right-of-Way (ROW), 2. The TBMencounters unanticipated geological features or utilities in urbansettings, or 3. The tunnel alignment and correction curves exceed thetight tolerances required for sustaining the dynamic envelope of traintunnels and highway tunnels. In order to avoid negative impacts on theTBM, the tunnel surroundings, or underground utilities, it is imperativethat TBM be precisely locatable and guided when boring through theearth.

In addition to the need for precise navigation of the TBM, the tunnelitself must be mapped. The need for mapping in tunnels is twofold.Firstly, an as-built map of the tunnel is needed to compare finishedtunnel dimensions to plan requirements. Secondly, the as-built map canbe maintained after the tunnel is completed and used as a baselinemeasurement for reference during subsequent surveys to observe changesin tunnel geometry over time.

The present methods of TBM guidance primarily use lasers andconventional surveying techniques. Lasers and transit theodolites,originating from the tunnel entrance, are relayed through a network offixed monuments on the tunnel walls and used to identify the positionand attitude of the TBM relative to the desired design location. Theprecision in identifying the exact location (Northing, Easting,Elevation) of this progressive series of monuments and their growingerror as the tunnel extends can lead to improper alignment of the tunnelor missing the end target within the stipulated tolerance. Thisconventional system using sighted theodolites to advance the monumentsused by the laser guidance systems is often adversely affected by errorinherent to accuracy of the measuring instruments, light refraction,angle of incidence, and reception. From the final measured monument nearthe TBM, a servo theodolite with distance measuring capability, alongwith inclinometers on the TBM, are used to identify the axis of the TBMas well as monitor TBM pitch (up and down), yaw, and rotation dependingon their installation orientation. The theodolite locates and reports tothe underlying guidance computer prisms attached to the TBM with a knownorientation and location relative to the reference frame of the TBM. Themotorized station can measure their location as the TBM bores thetunnel. The output from the inclinometers and updated target locationsis relayed to a central processing unit which outlines the path for theTBM. Monitoring of TBM vertical alignment is derived from the samemethods of angle and distance measurement. The series of monumentsaffixed to the tunnel wall as the TBM advances is measured for elevationusing wire line water level instruments to minimize the accumulation oferror relative to elevation. Gyroscopes may also be used to monitor theyaw of the TBM, verified by a surveyor.

The present state of tunnel mapping utilizes a two-step method. Firstly,the mapping positions are precisely located in reference to a knownpoint outside of the tunnel. This is accomplished using a theodolitemeasurement device. If the tunnel curves, mirrors are used to reflectthe beam, and the mirrors' locations are measured by the lasermeasurement device. Each of these mirrors induces additional error inthe final measurement of the mapping positions. With the location andorientation of the mapping stations known, the tunnel walls are thenmeasured at several locations with respect to this position. Thesemeasurements are typically done using reflector-less laser measurementsystem; however, other touch-less measurement systems, such asElectronic Distance Measurements (EDM), may be used to measure thedistance to the tunnel walls.

The process of establishing the mapping locations and obtainingmeasurement is repeated until the entire tunnel has been measured. Thedistance measurements are then associated with their respectivelocations to generate a three dimensional map of the tunnel. Thisprocess is costly, time-consuming, and labor-intensive, requiringcessation of any work and traffic in the tunnel until survey completion.

What is needed is an integrated navigation system that providesreal-time parametric guidance information to the TBM, relative to thetunnel origin (hereinafter “the pit”), past course, and currenttrajectory, while simultaneously employing a non-contact measuringsystem in concert with said origin and course information for the finalprovision of an as-built map of tunnel dimensions and centerline. Thepit is a known point within the earth-centered-earth fixed globalpositioning system (GPS), and at least one of GPS retransmission andtime modulated wireless triangulation architectures provide availabilityof positioning signals in the otherwise unavailable undergroundenvironment of a newly excavated tunnel. As the TBM proceeds along itsexcavation heading, a vehicle such as a rubber wheeled vehicle or alocomotive delivers ring assemblies, fabricated on-site in the pit tosupport the recently excavated portion of the tunnel. The constrainedcurvilinear path, also known as the design centerline, from pit to TBMis regularly traversed by the locomotive which is, in current systems,employed for transport of ring assemblies and muckout.

DESCRIPTION OF RELATED ART

In a discussion of prior art, European patent application Ser. No.EP20010304645 filed May 25, 2001, titled SELF-CONTAINED MAPPING ANDPOSITIONING SYSTEM UTILIZING POINT CLOUD DATA generally describes aself-contained mapping and positioning system for underground miningthat is capable of mapping the topography of a region, such as a minetunnel, and further being able to use the mapped data to determine theposition of an object, such as a mining vehicle, within the mine tunnel.

The method described in European patent application Ser. No.EP20010304645 provides only the position of the object, whereas thepresent invention incorporates positioning as well as automatic coursecorrection as determined by a pre-established path. Furthermore, thepresent invention includes permanent monuments to be used in post-boringsurveys to evaluate changes in tunnel geometry.

In a discussion of prior art, U.S. patent application Ser. No.08/304,858 filed Sep. 13, 1994, titled GUIDANCE SYSTEM AND METHOD FORKEEPING A TUNNEL BORING MACHINE CONTINUOUSLY ON A PLAN LINE generallydescribes a guidance system and method for keeping a TBM continuously ona plan line. The guidance system requires no machine operatorcalculations and provides the boring machine operator with a graphicdisplay of past, present, and projected positions of the boring machinefrom a horizontal and vertical perspective. The system uses a laser beamtransmitter placed to the rear of the TBM along with a front opaquetarget with a horizontal and vertical cross-hair and a rear transparenttarget with a horizontal and vertical cross-hair. The front and reartargets are disposed on the front and the rear of the boring machine.Also, an on-board programmable computer is installed on the boringmachine for imputing data as to horizontal offset and vertical offsetreadings from the front and rear targets as the boring machine advancesforward. Typically the boring machine moves forward in increments offour feet with offset readings taken by the operator after eachincrement. The offsets are measured in feet up to two decimal placeswith the readings based on measured positions being wither right or leftof the vertical cross-hair and above or below the horizontal cross-hairof the front and rear targets. Further, the on-board computer isprogrammed to store and provide a laser alignment check for verifyinglaser setup information and to graphically display alignment errorsduring a change in the setup of the laser beam transmitter by a surveycrew.

The device described in U.S. patent application Ser. No. 08/304,858employs a series of lasers to project the path which are prone to errorinherent to light refraction, angle of incidence, and reception. Thepresent invention provides the TBM with data via the locomotive,combined with an on-board INS to provide the TBM with the currentorientation, direction, and position to compile the projected path andcompare with the desired path.

In a discussion of prior art, U.S. Pat. No. 3,498,673 filed Feb. 19,1968, titled MACHINE GUIDANCE SYSTEM AND METHOD generally describes aTBM disposed within a tunnel and provided with a guidance systemcomprising a laser projection unit fixedly supported by a wall of thetunnel and directing its beam onto a mirror-like reflector mounted onthe machine, whereby the reflector provides a reflection of the beam ona target also mounted on the machine. The tunnel boring apparatus issteered to maintain the reflection at a predetermined location on thetarget.

The method described in U.S. Pat. No. 3,498,673 aligns external laserswith the desired path and is sent through the TBM which is steered suchthat the TBM keeps the laser within a designated area. Laser guidancesystems are prone to error inherent to light refraction, angle ofincidence, and reception. The present invention utilizes a locomotivewith an INS to position the TBM continuously; this position is thencompared to the desired path as programmed into the TBM to provide theTBM with a path that needs to be followed to match the desired path.

In a discussion of prior art, European patent application Ser. No.EP20030250157 filed Jan. 10, 2003, titled METHOD AND APPARATUS FORSURVEYING THE GEOMETRY OF TUNNELS generally describes a method andapparatus for surveying the geometry of tunnels comprising measuring theposition of a tunnel surface relative to an absolute three-dimensionalcoordinate system, using at least one reflector-less distance sensormounted for orientation in three dimensions and calculating a deviationfrom a predefined geometry for the surface and displaying said deviationin real time.

The method and apparatus described in European patent application Ser.No. EP20030250157 is well suited to the mapping of tunnels as well aspost-boring surveys for maintenance of the tunnel but is not well suitedfor as-built mapping during the tunneling process. The present inventionutilizes permanent monuments for long-term tunnel mapping, utilizes anINS system that is integrated with the self-contained mapping system,and communicates the combined parametric information to the TBM toprovide navigational guidance for the TBM.

So as to reduce the complexity and length of the Detailed Specification,and to fully establish the state of the art in certain areas oftechnology, Applicant(s) herein expressly incorporate(s) by referenceall of the following materials identified in each numbered paragraphbelow. The incorporated materials are not necessarily “prior art” andApplicant(s) expressly reserve(s) the right to swear behind any of theincorporated materials.

U.S. Pat. No. 6,707,424 Integrated Positioning System and Method

U.S. Pat. No. 8,417,490 System and Method for the Configuration of anAutomotive Vehicle with Modeled Sensors

Inertial Navigation, by Kevin J. Walchko, University of Florida,Gainesville, Fla., and Dr. Paul A. C. Mason, NASA Goddard Space FlightCenter, Greenbelt Md. Published 2002.

Real-Time Tunnel Boring Machine Monitoring: A State of the Art Review,by Michael A. Mooney, Bryan Walter, Christian Frenzel. Colorado Schoolof Mines, Golden Co. Published 2012

Design and Field Testing of an Autonomous Underground Training System,by Joshua A. Marshall and Timothy D. Barfoot. Published Dec. 13, 2007

Applicant(s) believe(s) that the material incorporated above is“non-essential” in accordance with 37 CFR 1.57, because it is referredto for purposes of indicating the background of the invention orillustrating the state of the art. However, if the Examiner believesthat any of the above-incorporated material constitutes “essentialmaterial” within the meaning of 37 CFR 1.57(c)(1)-(3), applicant(s) willamend the specification to expressly recite the essential material thatis incorporated by reference as allowed by the applicable rules.

SUMMARY OF THE INVENTION

Although the best understanding of the present invention will be hadfrom a thorough reading of the specification and claims presented below,this summary is provided in order to acquaint the reader with some ofthe new and useful features of the present invention. Of course, thissummary is not intended to be a complete litany of all of the featuresof the present invention, nor is it intended in any way to limit thebreadth of the claims, which are presented at the end of the detaileddescription of this application.

The present invention employs the regular traverse of the locomotivebetween the pit and the TBM as the method for accumulation of parametricguidance information to the TBM, relative to the tunnel origin, pastcourse, and current trajectory; the means of transmission of saidinformation to the TBM for navigation; and the means by which anon-contact measuring system is deployed in concert with said origin andcourse information for the final provision of an as-built map of tunneldimensions and centerline.

To implement the present invention, an integrated system of devices isinstalled in the pit, on the locomotive, on the TBM, and on the tunnelring assemblies. Installed in the pit are GPS receivers and GPSre-transmitters. Installed on the locomotive are GPS receivers for theretransmitted signals within the pit, a fault-tolerant inertialnavigation system (FTINS) that obtains course information, aself-contained mapping system, a central processing unit (CPU), and awireless transmitter for information transfer to and from the TBM.Installed on the TBM are a transceiver to receive transmitted origin andcourse information, a microprocessor and attitude heading referencesystem (AHRS) for calculation of heading, and various input/outputdevices. Included in the present invention are permanent monumentsaffixed to tunnel ring assemblies which are utilized in concert with theaforementioned self-contained mapping system installed on thelocomotive, as well as being available for post-boring surveys of tunnelgeometry.

Aspects and applications of the invention presented here are describedbelow in the drawings and detailed description of the invention. Unlessspecifically noted, it is intended that the words and phrases in thespecification and the claims be given their plain, ordinary, andaccustomed meaning to those of ordinary skill in the applicable arts.The inventors are fully aware that they can be their own lexicographersif desired. The inventors expressly elect, as their own lexicographers,to use only the plain and ordinary meaning of terms in the specificationand claims unless they clearly state otherwise and then further,expressly set forth the “special” definition of that term and explainhow it differs from the plain and ordinary meaning. Absent such clearstatements of intent to apply a “special” definition, it is theinventors' intent and desire that the simple, plain and ordinary meaningto the terms be applied to the interpretation of the specification andclaims.

The inventors are also aware of the normal precepts of English grammar.Thus, if a noun, term, or phrase is intended to be furthercharacterized, specified, or narrowed in some way, then such noun, term,or phrase will expressly include additional adjectives, descriptiveterms, or other modifiers in accordance with the normal precepts ofEnglish grammar. Absent the use of such adjectives, descriptive terms,or modifiers, it is the intent that such nouns, terms, or phrases begiven their plain, and ordinary English meaning to those skilled in theapplicable arts as set forth above.

Further, the inventors are fully informed of the standards andapplication of the special provisions of 35 U.S.C. §112, ¶6. Thus, theuse of the words “function,” “means” or “step” in the DetailedDescription or Description of the Drawings or claims is not intended tosomehow indicate a desire to invoke the special provisions of 35 U.S.C.§112, ¶6, to define the invention. To the contrary, if the provisions of35 U.S.C. §112, ¶6 are sought to be invoked to define the inventions,the claims will specifically and expressly state the exact phrases“means for” or “step for, and will also recite the word “function”(i.e., will state “means for performing the function of [insertfunction]”), without also reciting in such phrases any structure,material or act in support of the function. Thus, even when the claimsrecite a “means for performing the function of . . . ” or “step forperforming the function of . . . ”, if the claims also recite anystructure, material or acts in support of that means or step, or thatperform the recited function, then it is the clear intention of theinventors not to invoke the provisions of 35 U.S.C. §112, ¶6. Moreover,even if the provisions of 35 U.S.C. §112, ¶6 are invoked to define theclaimed inventions, it is intended that the inventions not be limitedonly to the specific structure, material or acts that are described inthe preferred embodiments, but in addition, include any and allstructures, materials or acts that perform the claimed function asdescribed in alternative embodiments or forms of the invention, or thatare well known present or later-developed, equivalent structures,material or acts for performing the claimed function.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present invention may be derived byreferring to the detailed description when considered in connection withthe following illustrative figures. In the figures, like-referencenumbers refer to like-elements or acts throughout the figures. Thepresently preferred embodiments of the invention are illustrated in theaccompanying drawings, in which:

FIG. 1 is a perspective view of the locomotive in the launch pitaccording to the preferred embodiment of the present invention,depicting the physical method for the accumulation of locomotive initialgeo-location data at the launch pit and the retransmission of said datato the locomotive.

FIG. 2 is a block diagram of the components contained within FIG. 1.

FIG. 3 depicts an alternate embodiment using a robotic total surveystation mounted in the launch pit and a system of survey reflectors.

FIG. 4 is a block diagram depicting the components within FIG. 3 whichprovide for the accumulation and transmission of initial geo-locationdata.

FIG. 5 depicts an alternate embodiment using a robotic total surveystation mounted on the locomotive and a system of survey reflectors inthe launch pit.

FIG. 6 is a block diagram depicting the components contained within FIG.5 which provide for the accumulation and transmission of initialgeo-location data.

FIG. 7 depicts an alternate embodiment using a docking station andalignment marks at surveyed locations within the launch pit.

FIG. 8 is a block diagram depicting the components contained within FIG.7 which provide for the accumulation and transmission of initialgeo-location data.

FIG. 9 is a block diagram of the components contained within thelocomotive.

FIG. 10 is a block diagram illustrating the components contained withinthe TBM.

FIG. 11 depicts the process elements associated with the utilization ofgeo-location input data.

FIG. 12 depicts the process elements associated with the utilization ofupdated geo-location information.

FIG. 13 is a block diagram illustrating the components contained withinthe fault tolerant inertial navigation system.

FIG. 14 depicts the process elements accomplished within the faulttolerant inertial navigation system componentry.

FIG. 15 is a block diagram illustrating the components utilized for theself-contained mapping system.

FIG. 16 depicts the process elements accomplished by the self-containedmapping system.

FIG. 17 is a block diagram of the SACore IMM for Automatic Guidance of aTBM.

DETAILED DESCRIPTION

In the following description, and for the purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of the various aspects of the invention. It will beunderstood, however, by those skilled in the relevant arts, that thepresent invention may be practiced without these specific details. Inother instances, known structures and devices are shown or discussedmore generally in order to avoid obscuring the invention. In many cases,a description of the operation is sufficient to enable one to implementthe various forms of the invention, particularly when the operation isto be implemented in software. It should be noted that there are manydifferent and alternative configurations, devices and technologies towhich the disclosed inventions may be applied. The full scope of theinventions is not limited to the examples that are described below.

In the following examples of the illustrated embodiments, references aremade to the accompanying drawings which form a part hereof, and in whichis shown by way of illustration various embodiments in which theinvention may be practiced. It is to be understood that otherembodiments may be utilized and structural and functional changes may bemade without departing from the scope of the invention.

FIG. 1 illustrates the preferred embodiment of the physical method forthe accumulation of locomotive initial geo-location data within thelaunch pit 105 and the retransmission of said data to the locomotive100. The locomotive 100 starts and ends each travel cycle through thetunnel 110 in the launch pit 105. Surrounding the launch pit 105 arethree or more geo-location and retransmission devices 200. A locomotivemounted transceiver 115 receives and transmits the position data 225(FIG. 2).

FIG. 2 describes the components illustrated in FIG. 1. The geo-locationand retransmission devices 200 each contain at least one globalpositioning system (GPS) receiver 205, microprocessor 210, andtransmitter 215. These devices 200, well known in the art of positiondetermination and land survey techniques, are designed to receive a GPSsignal 220 by which position data 225 is determined and relayed to alocomotive mounted transceiver 115 (FIG. 1).

These commercially available systems utilize two positioning andnavigation systems in a single unit, the first is used within sight ofearth-orbiting Global Navigation Satellite System (GNSS) satellites andthe second in less than optimal GNSS locations. The locomotive 100(FIG. 1) in the launch pit 105 (FIG. 1) will generally not be inline-of-sight of the earth-orbiting satellites. The locomotive mountedtransceiver 115 (FIG. 1) receives transmissions from a group of at leastthree ground-based beacons stationed at precise known locations, each ofwhich transmits a distinct signal, including location information. Themeans of data transmission may include wireless protocols such as802.11g, Bluetooth, time modulated ultra-wide band (TM-UWB) or othersuch wireless methods as may arise with developing technologies.

Another embodiment of the present invention, illustrated in FIG. 3 anddescribed by FIG. 4, uses a pit mounted robotic total survey station 400comprising a robotic mount 405, a controller 410, and a total station415 (similar in method and construction to the Trimble Series 6), and asurvey station transceiver 420. The pit mounted robotic total surveystation 400 is mounted along the perimeter of the launch pit 105 whereit utilizes reflective monuments 310 outside of the launch pit 105 todetermine its geo-location. A locomotive mounted reflective monument 305is used to determine the location of the locomotive 100 relative to theknown reflective monuments 310. The survey station transceiver 420contained within the pit mounted robotic total survey station 400transmits position data 225 to the survey station transceiver 420 on thelocomotive 100.

According to yet another embodiment of the present invention,illustrated in FIG. 5 and described by FIG. 6, a locomotive mountedrobotic total survey station 600, comprising a robotic mount 405, acontroller 410, and total station 415. Within the method described bythis embodiment, the locomotive mounted robotic total survey station 600obtains the position of the locomotive 100 by triangulation with atleast three reflective monuments 310 at surveyed locations within thelaunch pit 105. As depicted in FIG. 9, the locomotive mountedtransceiver 115 is bypassed with this embodiment and position data 225is direct fed to self-contained mapping system (SCMS) 900 (FIG. 9)onboard the locomotive 100.

A less technical yet viable alternative embodiment of the presentinvention is illustrated in FIG. 7 and described by FIG. 8, wherein thelocomotive 100 stops at a fixed docking station 700 installed at theterminal point of the track in the pit 105. Alignment marks 705 may alsobe utilized in this embodiment either independently or in concert withthe fixed docking station 700 to establish position data 225. Thesurveyed origin point established by the docking station 700 is a directinput to central processing unit one (CPU1) 1520 (FIG. 15) onboard theSCMS 900 (FIG. 9), offsets to the inertial navigation system (INS) arecalculated and the INS is updated to the current position and time ofposition.

Referring now to FIG. 9, there is illustrated an embodiment of thepresent invention describing the componentry mounted on and within thebody of the locomotive 100 for the gathering and storage of informationrelated to the guidance of the TBM 1000 (FIG. 10) and the mapping of thetunnel 110 (FIGS. 1, 3, 5, 7). Within the preferred embodiment of FIG. 1and the alternate embodiment of FIG. 3, a locomotive mounted transceiver115 establishes a link by which position data 225 (FIGS. 2, 4, 6, and 8)is transferred to and received by CPU1 1520 (FIG. 15). In the case ofthe alternate embodiments of FIGS. 5 and 7, initial position data isdirectly provided to CPU1 1520 (FIG. 15). In each of the describedembodiments, the position data 225 (FIGS. 2, 4, 6, and 8) from the SCMS900 is utilized to either initialize or recalibrate fault tolerantinertial navigation system one (FTINS1) 1515 (FIG. 15) such that a pointof origin, also known as initial position, within the launch pit(Position 1) is calculated. Data is transmitted from CPU1 920 via SCMStransmitter 1505 (FIG. 15) to the host PC 910 and to the TBM 1000 (FIG.10). The data transmitted to the host PC 910 may be stored on internetbased storage.

Referring now to FIG. 10, the docking process 1135 (FIG. 11) within anembodiment of the present invention establishes the actual position 1020relative to the locomotive 100 (FIGS. 1, 2, 3, and 7), which is bothnecessary and sufficient to characterize the actual underground locationof the TBM 1000. Central processing unit 2 (CPU2) 1010 receives updatedposition information (Position 2) 1015 from CPU1 1520 (FIG. 15) throughTBM transceiver 1005. The TBM 1000 obtains the presumed position(Position 3) 1025 from FTINS2 1030 based on movement of the TBM 1000(element 1200). CPU2 1010 calculates the difference between the updatedposition information (Position 2) 1015 and the presumed position(Position 3) 1025 (element 1205), and the difference is applied as acorrection to the Position 3 1025 data in FTINS2 1030 (element 1210).

FIG. 11 depicts the process elements associated with the utilization ofposition input data provided by the componentry depicted in FIG. 1, theinteraction of componentry depicted in FIG. 9 as the locomotive 100transits the curvilinear tunnel path 1125, and the transmission ofaccumulated data to the TBM 1000 componentry of FIG. 10. InitialPosition 0 is established at the geo-location and retransmission devices200 (element 1100). The time modulated signal with Position 0 data isthen transmitted to CPU1 1520 (FIG. 15) onboard the locomotive 100(element 1105). CPU1 1520 (FIG. 15) calculates initial position of thelocomotive 100 (Position 1 1400) (element 1110). The Position 1 1400data is then provided to FTINS1 1515 (FIG. 15) (element 1115). FTINS11515 (FIG. 15) is then updated to Position 1 1400 (element 1120).

Two processes occur as the locomotive 100 (FIGS. 1, 3, 5, and 7)transits a curvilinear path 1120 through the tunnel 110 (FIGS. 1, 3, 5,and 7) from the launch pit 105 (FIGS. 1, 3, 5, and 7) to the TBM 1000(FIG. 10). The first process is the establishment of guidanceinformation for the TBM 1000 (FIG. 10) by the delivery of a positionupdate to the FTINS2 1030 (FIG. 10). The second process is the activemapping of the tunnel 110 (FIGS. 1, 3, 5, and 7) as-built, accomplishedby the measurement of distance to the tunnel walls 1130 by a SCMS 900(FIGS. 9 and 15). These processes may be accomplished simultaneouslyduring the transit from launch pit 105 (FIGS. 1, 3, 5, and 7) to dockingwith the TBM at Position 2 1135 or separately so as to focus on deliveryof position data to the TBM 1000 (FIG. 10) on the incoming trip and tofocus on tunnel mapping on the outgoing trip.

Within these two processes, whether simultaneous or separate,information from FTINS1 1515 (FIG. 15) and the SCMS 900 (FIGS. 9 and 14)is provided to CPU1 1520 (FIG. 15), which compiles the aforementioneddata with the initial position data 1120. Upon docking 1135 with the TBM1000 (FIG. 10), the FTINS microprocessor 1320 (FIG. 13) calculates theactual position and provides this data to the onboard CPU1 1520 (FIG.15) (element 1140). The docking process 1135 establishes actual positionrelative to the locomotive 100 (FIGS. 1, 3, 5, and 7) and the TBM 1000(FIG. 10). All information relative to travel and tunnel measurementfrom the SCMS 900 (FIG. 9) is retained onboard the locomotive 100 (FIGS.1, 3, 5, and 7) until its return to the launch pit 105 (FIGS. 1, 3, 5,and 7), where data collected in terms of TBM 1000 (FIG. 10) position andtunnel measurements are uploaded to the host PC 910 (FIG. 9) and may bebacked up to internet-based data storage.

Referring now to FIG. 12, the TBM 1000 (FIG. 10) obtains the presumedposition (Position 3) 1025 (FIG. 10) from FTINS2 1030 (FIG. 10) based onmovement of the TBM 1000 (FIG. 10) (element 1200). CPU2 1010 (FIG. 10)calculates the difference between the updated position information(Position 2) 1015 (FIG. 10) and the presumed position (Position 3) 1025(FIG. 10) (element 1205), and the difference is applied as a correctionto the Position 3 1025 (FIG. 10) data in FTINS2 1030 (FIG. 10) (element1210). There is programmed within CPU2 1010 (FIG. 10) a known offsetdistance 1215 from FTINS2 1030 (FIG. 10) to the steering control pointof the TBM 1000 (FIG. 10), and CPU2 1010 (FIG. 10) applies theaforementioned known offset to the Position 3 1025 (FIG. 10) data(element 1220) contained within FTINS2 1030 (FIG. 10). The designedtunnel route 1225 programmed within the read-only memory of CPU2 1010(FIG. 10), and the as-designed tunnel route 1225 is now compared to thecurrent position 1230. CPU2 1010 (FIG. 10) on the TBM 1000 (FIG. 10) nowestablishes a new immediate heading 1235 for the TBM 1000 (FIG. 10) tofollow. This new heading is applied to a steering correction 1240 to theTBM 1000 (FIG. 10) for either a manual or automatic pilot to follow.

FIG. 13 depicts the components in the fault tolerant inertial navigationsystem (FTINS) 1300. Both the locomotive 100 (FIGS. 1, 3, 5, and 7) andthe TBM 1000 (FIG. 10) are equipped with two inertial measurement units(IMUs) 1305 which include one or more angular rate sensors (gyroscopes)1310 and one or more accelerometers 1315 which provide information tothe FTINS microprocessor 1320. The output of the FTINS microprocessor1320 describes the physical location of the locomotive 100 (FIGS. 1, 3,5, and 7) relative to the known initial position data 225 (FIGS. 2, 4,6, and 8), and the physical location of the TBM 1000 (FIG. 10) relativeto its last position update.

FIG. 14 depicts the process elements accomplished within the FTINS 1300(FIG. 13) componentry. The gyroscopes provide (ω_(x), ω_(y), ω_(z)) 1405and the accelerometers provide ({umlaut over (x)}, ÿ, {umlaut over (z)})1410 data. The FTINS microprocessor 1320 (FIG. 13) calculates change inposition by integrating the acceleration data 1415. The FTINSmicroprocessor 1320 (FIG. 13) then receives the initialized position(Position 1) 1400 and compares it to the change in position 1420. TheFTINS microprocessor 1320 (FIG. 13) finally calculates the currentposition 1425.

Referring now to FIG. 15, there is illustrated an embodiment of thepresent invention describing the self-contained mapping system (SCMS)900 mounted within the locomotive 100 (FIGS. 1, 3, 5, and 7). The SCMS900 uses a vibration isolation device 1510 and reflective monuments 310installed on, or cast into, the tunnel walls to map the tunnels. Thevibration isolation device 1510 comprises: FTINS1 1515; a contact-free3D scanner 1525, such as a Light Detection and Ranging or Laser ImagingDetection and Ranging (LiDAR); CPU1 1520; a data storage unit 1530, suchas a hard disk drive or a flash memory device; and a wired or wirelesstransmitter 1505. The SCMS 900 is capable of generating a 3D map of thetunnel 110 (FIGS. 1, 3, 5, and 7) during and after the tunnel boringprocess, generating an accurate measurement of the tunnel centerline,and, through the use of the reflective monuments 310 (FIGS. 3, 4, 5, and6), the SCMS 900 may be used to observe the movement of fixed points onthe tunnel wall during and after the tunnel boring process to determinechange in tunnel geometry over time.

FIG. 16 depicts the process elements accomplished by the SCMS 900 (FIGS.9 and 15). The SCMS 900 (FIGS. 9 and 15) applies a position offset tothe FTINS1 1515 (FIG. 15) measurements in order to match the FTINS1 1515(FIG. 15) reference frame with that of the 3D scanner 935 (FIGS. 9 and15). The SCMS 900 (FIGS. 9 and 15) then measure the distance to thetunnel wall relative to the locomotive 100 (FIGS. 1, 3, 5, and 7) andgeometric tunnel center 1600. The FTINS1 1515 (FIG. 15) estimates theabsolute position of the locomotive 100 (FIGS. 1, 3, 5, and 7) (element1605). Timestamps 1610, 1615 are applied to the two measurements and thedata is stored. A position offset to the FTINS1 1515 (FIG. 15) isapplied to the laser reference frame 1625. The data resulting from 1610and 1625 is then correlated 1620 and used to: generate the geometriccenterline of the tunnel 1630, extract reflective monument measurements1635, and generate a 3D mesh of the tunnel wall 1640. The position ofthe reflective monuments 310 (FIGS. 3, 4, 5, and 6) can be extracted1635 by isolating measurements from the 3D scanner 1525 (FIG. 15) whichindicate a higher reflectivity, and individual reflective monuments canbe identified by their specific reflectivity. Upon completing the tripthrough the tunnel, the SCMS 900 (FIGS. 9 and 15) uploads themeasurements from the FTINS1 1515 (FIG. 15) and 3D scanner 1525 (FIG.15) to an external computer via wired or wireless link 1645 for analysisof the as-built tunnel geometry.

Multi-Sensor Data Fusion:

Those skilled in the art of state estimation, robotics, and advanceddefense avionics understand academically that sensor-fusion is the artof combining sensory data or data derived from disparate sources suchthat the resulting information is in some sense “better” than would bepossible when these sources were used individually. This process ispredicated on the covariance (or the measure of how much two variablesvary together) of non-independent sources. The term “better” in the caseabove can mean more accurate, more complete, more dependable, or referto the result of an emerging view or state estimation.

The data sources for a fusion process are not specified to originatefrom identical sources or sensors which may or may not be spatially andtemporally aligned. Further one can distinguish direct fusion, indirectfusion, and fusion of the outputs of the former two. Direct fusion isthe fusion of sensor data from a set of heterogeneous or homogeneoussensors, soft sensors, and history values of sensor data, while indirectfusion uses information sources like a prior knowledge about theenvironment and human input. Sensor fusion is also known as“multi-sensor data fusion” and is a subset of information fusion throughan implementation of the probability theory.

Probability theory is the mathematical study of phenomena characterizedby randomness or uncertainty. More precisely, probability is used formodeling situations when the result of a measurement, realized under thesame circumstances, produces different results. Mathematicians andactuaries think of probabilities as numbers in the closed interval from0 to 1 assigned to “events” whose occurrence or failure to occur israndom. Two crucial concepts in the theory of probability are those of arandom variable and of the probability distribution of a randomvariable.

Implementing the features described above with affordable instrumentsrequires reliable real-time estimates of system state. Unfortunately,the complete state is not always observable. State Estimation takes allthe data obtained and uses it to determine the underlying behavior ofthe system at any point in time. It includes fault detection, isolationand continuous system state estimation.

There are two parts to state estimation: modeling and algorithms. Theoverall approach is to use a model to predict the behavior of the systemin a particular state, and then compare that behavior with the actualmeasurements from the instruments to determine which state or states isthe most likely to produce the observed system behavior.

This is not well understood or currently implemented in the constructionindustry; the approach understood and practiced is logical decisions inlinear and deterministic systems. If use cases require higherconfidences in measurements, instrument specifications are tightenedresulting in the undesired effect of cost and schedule increases. Theenvironment we live and operate in is neither linear nor deterministic;use cases are infinite; and the perverse variability of the systems andpotential errors cannot be modeled. The variability of the problemidentified above includes aspects other than just spatial (i.e. preciselocation of the tunnel boring machine); temporal relationships are partof the fundamental intellectual structure (together with space andnumber) within which events must be sequenced, quantify the duration ofevents, quantify the intervals between them, and compare the kinematicsof objects.

In any of the embodiments listed above; the use of Fusion Engine (FE)and Kalman filters in the guidance system of the TBM, will greatlyimprove position accuracy and reduce instrument costs. The FEcontinuously receives measurements from multiple sources and generates astate estimate and covariance (confidence) of the current position ofthe TBM; all updated position data measurements received are used toensure the measurement data is within the FE estimates.

In order to continuously and accurately estimate the position of the TBMthe Kalman filters in the preferred embodiment are implemented as anasynchronous n-scalable Interacting Multiple Model (IMM) estimationFilter. The IMM comprises multiple models of drift from position inorder to accurately match the maneuvering and drift expectations.

Since the drift or progression of the gyros in either FTINS is not knownahead of time, an accurate model cannot be designed, so errors in theposition estimation will occur. Adding process noise to model the TBMmaneuvers or using a maneuver detector to adapt the filter has been usedin the art, but detection delays and large estimation errors duringmaneuvers are still a problem. It is generally accepted that theInteracting Multiple Model (IMM) estimator provides superior trackingperformance compared to a single Kalman Filter.

The IMM is based on using several models in parallel to estimate themaneuvering TBM's states. Each Kalman Filter, uses a different model foreach maneuver, one models a constant state of the TBM, another models aposition change in the longitudinal axis while another models a positionchange in the lateral axis and vertical axis. Switching between thesemodels during each sample period is determined probabilistically. Unlikemaneuver detection systems where only one filter model is used at atime, the IMM uses all filters. The overall state estimate output is aweighted combination of the estimates from the individual filters. Theweighting is based on the likelihood that a filter model is the correctmaneuvering TBM model.

The IMM estimator is a state estimation algorithm that uses Markovianswitching coefficients. A system with these coefficients is described byr models, M¹, M², . . . , M^(r), and given probabilities of switchingbetween these models. M^(j)(k) denotes that model j (M^(j)) is in effectduring the sampling period ending at time t_(k), [t_(k-1), t_(k)]. Thedynamics and measurement for a linear system are given by

x(k)=Φ^(j)(k,k−1)x(k−1)+G ^(j)(k,k−1)w ^(j)(k−1),  (1)

and

z(k)=H ^(j)(k)x(k)+v ^(j)(k),  (2)

where x(k) is the system state at time t_(k), z(k) is the measurementvector at time t_(k), Φ^(j)(k,k−1) is the state-transition matrix fromtime t_(k-1) to time t_(k) for M^(j)(k), G^(j)(k,k−1) is the noise inputmatrix, and H^(j)(k) is the observation matrix for M^(j)(k). The processnoise vector w^(j)(k−1) and the measurement noise vector v^(j)(k) aremutually uncorrelated zero-mean white Gaussian processes with covariancematrices Q^(j)(k−1) and R^(j)(k) respectively.

The initial conditions for the system state under each model j areGaussian random variables with mean x ^(j)(0) and covariance P^(j)(0).These prior statistics are assumed known, as also is μ^(j)(0)=Pr{M^(j)(0)}, which is the initial probability of model j at t₀.

The model switching is governed by a finite-state Markov chain accordingto the probability π_(ij)=Pr{M^(j)(k)|M^(i)(k−1)} of switching fromM^(i)(k−1) to M^(j)(k). The model switching probabilities, π_(ij), areassumed known and an example is

$\begin{matrix}{\pi_{ij} = {\begin{bmatrix}{.95} & {.05} \\{.05} & {.95}\end{bmatrix}.}} & (3)\end{matrix}$

A block diagram of the IMM estimator with only two models, forsimplicity, is shown in FIG. 17.

The inputs to the IMM estimator are {circumflex over (x)}¹(k−1|k−1),{circumflex over (x)}²(k−1|k−1), P¹(k−1|k−1), P²(k−1|k−1), andμ^(i|j)(k−1|k−1), all from the sampling period ending at t_(k-1). Where{circumflex over (x)}¹(k−1|k−1) is the state estimate from filter 1 attime t_(k-1) using measurements from time t_(k-1) and P¹(k−1|k−1) is thecorresponding state covariance matrix. Each of the filters use adifferent mixture of {circumflex over (x)}¹(k−1|k−1) and {circumflexover (x)}²(k−1|k−1) for their input, For r models, this mixing allowsthe model-conditioned estimates in the current cycle to be computedusing r filters rather than r² filters, which greatly decreases thecomputational burden. The inputs to the filters, {circumflex over(x)}⁰¹(k−1|k−1), {circumflex over (x)}⁰²(k−1|k−1), and the correspondingcovariance matrices are computed in the Interaction (Mixing) block.

For the filter matched to M^(j)(k), the inputs are

$\begin{matrix}{\mspace{79mu} {{{\hat{x}}^{0j}\left( {{k - 1}{k - 1}} \right)} = {\sum\limits_{i = 1}^{r}{{\mu^{i|j}\left( {{k - 1}{k - 1}} \right)}{{\hat{x}}^{i}\left( {{k - 1}{k - 1}} \right)}}}}} & (4) \\{{{P^{0j}\left( {{k - 1}{k - 1}} \right)} = {\sum\limits_{i = 1}^{r}{{\mu^{ij}\left( {{k - 1}{k - 1}} \right)}\left\{ {{P^{i}\left( {{k - 1}{k - 1}} \right)} + {\left\lbrack {{{\hat{x}}^{i}\left( {{k - 1}{k - 1}} \right)} - {{\hat{x}}^{0j}\left( {{k - 1}{k - 1}} \right)}} \right\rbrack \star \left\lbrack {{{\hat{x}}^{i}\left( {{k - 1}{k - 1}} \right)} - {{\hat{x}}^{0j}\left( {{k - 1}{k - 1}} \right)}} \right\rbrack^{T}}} \right\}}}},} & (5)\end{matrix}$

where the conditional model probability is

$\begin{matrix}\begin{matrix}{{\mu^{ij}\left( {{k - 1}{k - 1}} \right)} = {\Pr \left\{ {{{M^{i}\left( {k - 1} \right)}{M^{j}(k)}},Z_{1}^{k - 1}} \right\}}} \\{{= {\frac{1}{\mu^{j}\left( {k{k - 1}} \right)}\pi_{ij}{\mu^{i}\left( {{k - 1}{k - 1}} \right)}}},}\end{matrix} & (6)\end{matrix}$

and the predicted model probability is

$\begin{matrix}\begin{matrix}{{\mu^{j}\left( {k{k - 1}} \right)} = {\Pr \left\{ {{M^{j}(k)}Z_{1}^{k - 1}} \right\}}} \\{= {\sum\limits_{i = 1}^{r}{\pi_{ij}{{\mu^{i}\left( {{k - 1}{k - 1}} \right)}.}}}}\end{matrix} & (7)\end{matrix}$

Using the measurements, z(k), for the filter matched to M^(j)(k), theupdates are computed using the familiar Kalman Filter equations

{circumflex over (x)} ^(j)(k|k−1)=Φ^(j)(k,k−1){circumflex over (x)}⁰¹(k|k−1),  (8)

P ^(j)(k|k−1)=Φ(k,k−1)P ^(0j)(k|k−1)[Φ^(j)(k,k−1)]^(T) +G ^(j)(k,k−1)Q^(j)(k−1)[G ^(j)(k,k−1)]^(T)  (9)

v ^(j)(k)=z(k)−H(k){circumflex over (x)} ^(j)(k|k−1),  (10)

S ^(j)(k)=H ^(j)(k)P ^(j)(k|k−1)[H ^(j)(k)]^(T) +R ^(j)(k),  (11)

K ^(j)(k)=P ^(j)(k|k−1)[H ^(j)(k)]^(T) [S ^(j)(k)]⁻¹,  (12)

{circumflex over (x)} ^(j)(k|k)={circumflex over (x)} ^(j)(k|k−1)+K^(j)(k)v ^(j)(k),  (13)

P ^(j)(k|k)=[I−K ^(j)(k)H ^(j)(k)]P ^(j)(k|k−1),  (14)

where {circumflex over (x)}^(j)(k|k−1) is the predicted state estimateunder M^(j)(k), P^(j)(k|k−1) is the corresponding prediction covariance,v^(j)(k) is the residual, S^(j)(k) is the residual covariance matrix,K^(j)(k) is the Kalman gain matrix, {circumflex over (x)}^(j)(k|k) isthe updated state estimate under M^(j)(k), and P^(j)(k|k) is the updatedcovariance matrix.

The likelihood of the filter matched to M^(j)(k) is defined byΛ^(j)(k)=f[z(k)|M^(j)(k), Z₁ ^(k-1)], where f[|] denotes a conditionaldensity. Using the assumption of Gaussian statistics, the filterresidual and the residual covariance, the likelihood is

$\begin{matrix}{{\Lambda^{j}(k)} = {\frac{1}{\sqrt{\det \;\left\lbrack {2\; \pi \; {S^{j}(k)}} \right\rbrack}}\exp {\left\{ {{- {{\frac{1}{2}\left\lbrack {v^{j}(k)} \right\rbrack}^{T}\left\lbrack {S^{j}(k)} \right\rbrack}^{- 1}}{v^{j}(k)}} \right\}.}}} & (15)\end{matrix}$

The probability for M^(j)(k) is

$\begin{matrix}{{{\mu^{j}\left( {kk} \right)} = {{\Pr \left\{ {{M^{j}(k)}Z_{1}^{k}} \right\}} = {\frac{1}{c}{\mu^{j}\left( {k{k - 1}} \right)}{\Lambda^{j}(k)}}}},} & (16)\end{matrix}$

where the normalization factor c is

$\begin{matrix}{c = {\sum\limits_{j = 1}^{r}{{\mu^{i}\left( {k{k - 1}} \right)}{{\Lambda^{i}(k)}.}}}} & (17)\end{matrix}$

These computations are performed in the Model Probability Update block.Finally the combined state estimate {circumflex over (x)}(k|k) and thecorresponding state error covariance for the IMM are given by

$\begin{matrix}{\mspace{79mu} {{{\hat{x}\left( {kk} \right)} = {\sum\limits_{j = 1}^{r}{{\mu^{j}\left( {kk} \right)}{{\hat{x}}^{j}\left( {kk} \right)}}}},}} & (18) \\{{P\left( {kk} \right)} = {\sum\limits_{j = 1}^{r}{{\mu^{j}\left( {kk} \right)}{\left\{ {{P^{j}\left( {kk} \right)} + {\left\lbrack {{{\hat{x}}^{j}\left( {kk} \right)} - {\hat{x}\left( {kk} \right)}} \right\rbrack \left\lbrack {{{\hat{x}}^{j}\left( {kk} \right)} - {\hat{x}\left( {kk} \right)}} \right\rbrack}^{T}} \right\}.}}}} & (19)\end{matrix}$

The final state estimate, {circumflex over (x)}(k|k), is the bestestimate of the TBM state and P(k|k) is the error covariance matrix forthis optimal state estimate.

For the sake of convenience, the operations are described as variousinterconnected functional blocks or distinct software modules. This isnot necessary, however, and there may be cases where these functionalblocks or modules are equivalently aggregated into a single logicdevice, program or operation with unclear boundaries. In any event, thefunctional blocks and software modules or described features can beimplemented by themselves, or in combination with other operations ineither hardware or software.

Having described and illustrated the principles of the invention in apreferred embodiment thereof, it should be apparent that the inventionmay be modified in arrangement and detail without departing from suchprinciples. Claim is made to all modifications and variation comingwithin the spirit and scope of the following claims.

1. A dual purpose geo-location and dynamic mapping system for a tunnelboring machine (TBM), the system comprising: a TBM; a first InertialNavigation System (INS) mounted on a vehicle, wherein the vehicle isseparate from the TBM; a position determining system for determining anaccurate position of the vehicle; a first computer located on thevehicle, wherein the first computer is configured to collect data from aranging device as the vehicle traverses the tunnel and store the data ina first memory; a second INS located on the TBM connected to a secondcomputer and a second memory, wherein the second INS position data isupdated by the first INS.
 2. The system of claim 1, wherein the rangingdevice is mounted on the vehicle to make measurements orthogonal to thegeometric centerline of the tunnel.
 3. The system of claim 1, whereinthe first memory is located on the vehicle.
 4. The system of claim 1,wherein the stored data from the first memory is transmitted to thesecond memory when the vehicle arrives at a known and predeterminedlocation with the TBM.
 5. The system of claim 1, wherein the first INSis connected to the first computer.
 6. The system of claim 1, whereinthe second computer is configured to receive the stored data from thefirst computer.
 7. The system of claim 1, wherein the second computer isconfigured to receive an updated position of the first INS.
 8. Thesystem of claim 1, wherein the second computer is configured totranslate the first INS position to the second INS as a position update.9. The system of claim 1, wherein the second computer is configured todetermine real-time parametric guidance information for the TBM.
 10. Thesystem of claim 9, wherein the real-time parametric guidance informationcauses the TBM to at least one of maintain a current path and coursecorrect to the design centerline of the tunnel.
 11. A dual purposegeo-location and dynamic mapping method for a tunnel boring machine(TBM), the method comprising: operating a TBM; operating a firstInertial Navigation System (INS) mounted on a vehicle, wherein thevehicle is separate from the TBM; using a position determining system todetermine an accurate position of the vehicle; operating a firstcomputer located on the vehicle, wherein the first computer isconfigured to collect data from a ranging device as the vehicletraverses the tunnel and store the data in a first memory; using asecond INS located on the TBM connected to a second computer and asecond memory, wherein the second INS position data is updated by thefirst INS.
 12. The method of claim 11, wherein the ranging device ismounted on the vehicle to make measurements orthogonal to the geometriccenterline of the tunnel.
 13. The method of claim 11, wherein the firstmemory is located on the vehicle.
 14. The method of claim 11, whereinthe stored data from the first memory is transmitted to the secondmemory when the vehicle arrives at a known and predetermined locationwith the TBM.
 15. The method of claim 11, wherein the first INS isconnected to the first computer.
 16. The method of claim 11, wherein thesecond computer is configured to receive the stored data from the firstcomputer.
 17. The method of claim 11, wherein the second computer isconfigured to receive an updated position of the first INS.
 18. Themethod of claim 11, wherein the second computer is configured totranslate the first INS position to the second INS as a position update.19. The method of claim 11, wherein the second computer is configured todetermine real-time parametric guidance information for the TBM.
 20. Themethod of claim 19, wherein the real-time parametric guidanceinformation causes the TBM to at least one of maintain a current pathand course correct to the design centerline of the tunnel.